{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "#print?\n",
    "#!python -V\n",
    "!pip list"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['c:\\\\ProgramData\\\\Anaconda3\\\\envs\\\\python310\\\\lib\\\\site-packages\\\\numpy']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__path__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nNumPy\\n=====\\n\\nProvides\\n  1. An array object of arbitrary homogeneous items\\n  2. Fast mathematical operations over arrays\\n  3. Linear Algebra, Fourier Transforms, Random Number Generation\\n\\nHow to use the documentation\\n----------------------------\\nDocumentation is available in two forms: docstrings provided\\nwith the code, and a loose standing reference guide, available from\\n`the NumPy homepage <https://www.scipy.org>`_.\\n\\nWe recommend exploring the docstrings using\\n`IPython <https://ipython.org>`_, an advanced Python shell with\\nTAB-completion and introspection capabilities.  See below for further\\ninstructions.\\n\\nThe docstring examples assume that `numpy` has been imported as `np`::\\n\\n  >>> import numpy as np\\n\\nCode snippets are indicated by three greater-than signs::\\n\\n  >>> x = 42\\n  >>> x = x + 1\\n\\nUse the built-in ``help`` function to view a function\\'s docstring::\\n\\n  >>> help(np.sort)\\n  ... # doctest: +SKIP\\n\\nFor some objects, ``np.info(obj)`` may provide additional help.  This is\\nparticularly true if you see the line \"Help on ufunc object:\" at the top\\nof the help() page.  Ufuncs are implemented in C, not Python, for speed.\\nThe native Python help() does not know how to view their help, but our\\nnp.info() function does.\\n\\nTo search for documents containing a keyword, do::\\n\\n  >>> np.lookfor(\\'keyword\\')\\n  ... # doctest: +SKIP\\n\\nGeneral-purpose documents like a glossary and help on the basic concepts\\nof numpy are available under the ``doc`` sub-module::\\n\\n  >>> from numpy import doc\\n  >>> help(doc)\\n  ... # doctest: +SKIP\\n\\nAvailable subpackages\\n---------------------\\ndoc\\n    Topical documentation on broadcasting, indexing, etc.\\nlib\\n    Basic functions used by several sub-packages.\\nrandom\\n    Core Random Tools\\nlinalg\\n    Core Linear Algebra Tools\\nfft\\n    Core FFT routines\\npolynomial\\n    Polynomial tools\\ntesting\\n    NumPy testing tools\\nf2py\\n    Fortran to Python Interface Generator.\\ndistutils\\n    Enhancements to distutils with support for\\n    Fortran compilers support and more.\\n\\nUtilities\\n---------\\ntest\\n    Run numpy unittests\\nshow_config\\n    Show numpy build configuration\\ndual\\n    Overwrite certain functions with high-performance SciPy tools.\\n    Note: `numpy.dual` is deprecated.  Use the functions from NumPy or Scipy\\n    directly instead of importing them from `numpy.dual`.\\nmatlib\\n    Make everything matrices.\\n__version__\\n    NumPy version string\\n\\nViewing documentation using IPython\\n-----------------------------------\\nStart IPython with the NumPy profile (``ipython -p numpy``), which will\\nimport `numpy` under the alias `np`.  Then, use the ``cpaste`` command to\\npaste examples into the shell.  To see which functions are available in\\n`numpy`, type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use\\n``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow\\ndown the list.  To view the docstring for a function, use\\n``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view\\nthe source code).\\n\\nCopies vs. in-place operation\\n-----------------------------\\nMost of the functions in `numpy` return a copy of the array argument\\n(e.g., `np.sort`).  In-place versions of these functions are often\\navailable as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.\\nExceptions to this rule are documented.\\n\\n'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.__all__\n",
    "#np.__dict__\n",
    "np.__doc__\n",
    "#np.__file__\n",
    "#np.__package__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     002896.SZ   20231025  33.28  36.25  33.22  34.49      32.95    1.54   \n",
      "1     002896.SZ   20231024  32.75  34.32  32.58  32.95      32.75    0.20   \n",
      "2     002896.SZ   20231023  33.71  34.02  32.25  32.75      34.06   -1.31   \n",
      "3     002896.SZ   20231020  35.35  36.35  33.80  34.06      35.82   -1.76   \n",
      "4     002896.SZ   20231019  35.61  36.20  35.10  35.82      35.61    0.21   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "1487  002896.SZ   20170904  24.77  24.77  24.77  24.77      22.52    2.25   \n",
      "1488  002896.SZ   20170901  22.52  22.52  22.52  22.52      20.47    2.05   \n",
      "1489  002896.SZ   20170831  20.47  20.47  20.47  20.47      18.61    1.86   \n",
      "1490  002896.SZ   20170830  18.61  18.61  18.61  18.61      16.92    1.69   \n",
      "1491  002896.SZ   20170829  15.51  16.92  15.51  16.92      11.75    5.17   \n",
      "\n",
      "      pct_chg        vol      amount  \n",
      "0      4.6737  132801.00  464876.948  \n",
      "1      0.6107   80978.03  269829.700  \n",
      "2     -3.8462   75142.20  247467.441  \n",
      "3     -4.9135   75855.89  263989.558  \n",
      "4      0.5897   59515.00  211937.608  \n",
      "...       ...        ...         ...  \n",
      "1487   9.9900      74.00     183.298  \n",
      "1488  10.0100      54.00     121.608  \n",
      "1489   9.9900      36.00      73.692  \n",
      "1490   9.9900      26.00      48.386  \n",
      "1491  44.0000      75.00     126.618  \n",
      "\n",
      "[1492 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 导入tushare\n",
    "import tushare as ts\n",
    "# 初始化pro接口\n",
    "pro = ts.pro_api('134e2799d5b514483218d281d17de3a78bff4bbbf996cb9711227cac')\n",
    "\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"002896.SZ\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20231025,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     002896.SZ   20231025  33.28  36.25  33.22  34.49      32.95    1.54   \n",
      "1     002896.SZ   20231024  32.75  34.32  32.58  32.95      32.75    0.20   \n",
      "2     002896.SZ   20231023  33.71  34.02  32.25  32.75      34.06   -1.31   \n",
      "3     002896.SZ   20231020  35.35  36.35  33.80  34.06      35.82   -1.76   \n",
      "4     002896.SZ   20231019  35.61  36.20  35.10  35.82      35.61    0.21   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "1487  002896.SZ   20170904  24.77  24.77  24.77  24.77      22.52    2.25   \n",
      "1488  002896.SZ   20170901  22.52  22.52  22.52  22.52      20.47    2.05   \n",
      "1489  002896.SZ   20170831  20.47  20.47  20.47  20.47      18.61    1.86   \n",
      "1490  002896.SZ   20170830  18.61  18.61  18.61  18.61      16.92    1.69   \n",
      "1491  002896.SZ   20170829  15.51  16.92  15.51  16.92      11.75    5.17   \n",
      "\n",
      "      pct_chg        vol      amount  \n",
      "0      4.6737  132801.00  464876.948  \n",
      "1      0.6107   80978.03  269829.700  \n",
      "2     -3.8462   75142.20  247467.441  \n",
      "3     -4.9135   75855.89  263989.558  \n",
      "4      0.5897   59515.00  211937.608  \n",
      "...       ...        ...         ...  \n",
      "1487   9.9900      74.00     183.298  \n",
      "1488  10.0100      54.00     121.608  \n",
      "1489   9.9900      36.00      73.692  \n",
      "1490   9.9900      26.00      48.386  \n",
      "1491  44.0000      75.00     126.618  \n",
      "\n",
      "[1492 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock002896.csv\")\n",
    "df.to_excel(\"stock002896.xlsx\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>1482</th>\n",
       "      <th>1483</th>\n",
       "      <th>1484</th>\n",
       "      <th>1485</th>\n",
       "      <th>1486</th>\n",
       "      <th>1487</th>\n",
       "      <th>1488</th>\n",
       "      <th>1489</th>\n",
       "      <th>1490</th>\n",
       "      <th>1491</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>...</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "      <td>002896.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20231025</td>\n",
       "      <td>20231024</td>\n",
       "      <td>20231023</td>\n",
       "      <td>20231020</td>\n",
       "      <td>20231019</td>\n",
       "      <td>20231018</td>\n",
       "      <td>20231017</td>\n",
       "      <td>20231016</td>\n",
       "      <td>20231013</td>\n",
       "      <td>20231012</td>\n",
       "      <td>...</td>\n",
       "      <td>20170913</td>\n",
       "      <td>20170908</td>\n",
       "      <td>20170907</td>\n",
       "      <td>20170906</td>\n",
       "      <td>20170905</td>\n",
       "      <td>20170904</td>\n",
       "      <td>20170901</td>\n",
       "      <td>20170831</td>\n",
       "      <td>20170830</td>\n",
       "      <td>20170829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>33.28</td>\n",
       "      <td>32.75</td>\n",
       "      <td>33.71</td>\n",
       "      <td>35.35</td>\n",
       "      <td>35.61</td>\n",
       "      <td>36.09</td>\n",
       "      <td>37.31</td>\n",
       "      <td>37.42</td>\n",
       "      <td>38.52</td>\n",
       "      <td>39.09</td>\n",
       "      <td>...</td>\n",
       "      <td>39.91</td>\n",
       "      <td>36.28</td>\n",
       "      <td>32.98</td>\n",
       "      <td>29.98</td>\n",
       "      <td>27.25</td>\n",
       "      <td>24.77</td>\n",
       "      <td>22.52</td>\n",
       "      <td>20.47</td>\n",
       "      <td>18.61</td>\n",
       "      <td>15.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>36.25</td>\n",
       "      <td>34.32</td>\n",
       "      <td>34.02</td>\n",
       "      <td>36.35</td>\n",
       "      <td>36.2</td>\n",
       "      <td>36.49</td>\n",
       "      <td>37.8</td>\n",
       "      <td>38.96</td>\n",
       "      <td>39.14</td>\n",
       "      <td>40.3</td>\n",
       "      <td>...</td>\n",
       "      <td>39.91</td>\n",
       "      <td>36.28</td>\n",
       "      <td>32.98</td>\n",
       "      <td>29.98</td>\n",
       "      <td>27.25</td>\n",
       "      <td>24.77</td>\n",
       "      <td>22.52</td>\n",
       "      <td>20.47</td>\n",
       "      <td>18.61</td>\n",
       "      <td>16.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>33.22</td>\n",
       "      <td>32.58</td>\n",
       "      <td>32.25</td>\n",
       "      <td>33.8</td>\n",
       "      <td>35.1</td>\n",
       "      <td>35.5</td>\n",
       "      <td>35.11</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.4</td>\n",
       "      <td>38.3</td>\n",
       "      <td>...</td>\n",
       "      <td>39.91</td>\n",
       "      <td>36.28</td>\n",
       "      <td>32.98</td>\n",
       "      <td>29.98</td>\n",
       "      <td>27.25</td>\n",
       "      <td>24.77</td>\n",
       "      <td>22.52</td>\n",
       "      <td>20.47</td>\n",
       "      <td>18.61</td>\n",
       "      <td>15.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>34.49</td>\n",
       "      <td>32.95</td>\n",
       "      <td>32.75</td>\n",
       "      <td>34.06</td>\n",
       "      <td>35.82</td>\n",
       "      <td>35.61</td>\n",
       "      <td>36.45</td>\n",
       "      <td>38.06</td>\n",
       "      <td>37.42</td>\n",
       "      <td>38.8</td>\n",
       "      <td>...</td>\n",
       "      <td>39.91</td>\n",
       "      <td>36.28</td>\n",
       "      <td>32.98</td>\n",
       "      <td>29.98</td>\n",
       "      <td>27.25</td>\n",
       "      <td>24.77</td>\n",
       "      <td>22.52</td>\n",
       "      <td>20.47</td>\n",
       "      <td>18.61</td>\n",
       "      <td>16.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>32.95</td>\n",
       "      <td>32.75</td>\n",
       "      <td>34.06</td>\n",
       "      <td>35.82</td>\n",
       "      <td>35.61</td>\n",
       "      <td>36.45</td>\n",
       "      <td>38.06</td>\n",
       "      <td>37.42</td>\n",
       "      <td>38.8</td>\n",
       "      <td>39.45</td>\n",
       "      <td>...</td>\n",
       "      <td>36.28</td>\n",
       "      <td>32.98</td>\n",
       "      <td>29.98</td>\n",
       "      <td>27.25</td>\n",
       "      <td>24.77</td>\n",
       "      <td>22.52</td>\n",
       "      <td>20.47</td>\n",
       "      <td>18.61</td>\n",
       "      <td>16.92</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>1.54</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>0.21</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>0.64</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>...</td>\n",
       "      <td>3.63</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.69</td>\n",
       "      <td>5.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>4.6737</td>\n",
       "      <td>0.6107</td>\n",
       "      <td>-3.8462</td>\n",
       "      <td>-4.9135</td>\n",
       "      <td>0.5897</td>\n",
       "      <td>-2.3045</td>\n",
       "      <td>-4.2302</td>\n",
       "      <td>1.7103</td>\n",
       "      <td>-3.5567</td>\n",
       "      <td>-1.6477</td>\n",
       "      <td>...</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.01</td>\n",
       "      <td>10.02</td>\n",
       "      <td>10.01</td>\n",
       "      <td>9.99</td>\n",
       "      <td>10.01</td>\n",
       "      <td>9.99</td>\n",
       "      <td>9.99</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>132801.0</td>\n",
       "      <td>80978.03</td>\n",
       "      <td>75142.2</td>\n",
       "      <td>75855.89</td>\n",
       "      <td>59515.0</td>\n",
       "      <td>55764.69</td>\n",
       "      <td>99718.3</td>\n",
       "      <td>104992.73</td>\n",
       "      <td>111398.0</td>\n",
       "      <td>136569.47</td>\n",
       "      <td>...</td>\n",
       "      <td>1586.15</td>\n",
       "      <td>924.71</td>\n",
       "      <td>469.7</td>\n",
       "      <td>118.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>464876.948</td>\n",
       "      <td>269829.7</td>\n",
       "      <td>247467.441</td>\n",
       "      <td>263989.558</td>\n",
       "      <td>211937.608</td>\n",
       "      <td>200194.982</td>\n",
       "      <td>361372.791</td>\n",
       "      <td>399899.602</td>\n",
       "      <td>425743.454</td>\n",
       "      <td>532754.049</td>\n",
       "      <td>...</td>\n",
       "      <td>6330.324</td>\n",
       "      <td>3354.847</td>\n",
       "      <td>1549.07</td>\n",
       "      <td>353.764</td>\n",
       "      <td>310.65</td>\n",
       "      <td>183.298</td>\n",
       "      <td>121.608</td>\n",
       "      <td>73.692</td>\n",
       "      <td>48.386</td>\n",
       "      <td>126.618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 1492 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0          1           2           3           4     \\\n",
       "ts_code      002896.SZ  002896.SZ   002896.SZ   002896.SZ   002896.SZ   \n",
       "trade_date    20231025   20231024    20231023    20231020    20231019   \n",
       "open             33.28      32.75       33.71       35.35       35.61   \n",
       "high             36.25      34.32       34.02       36.35        36.2   \n",
       "low              33.22      32.58       32.25        33.8        35.1   \n",
       "close            34.49      32.95       32.75       34.06       35.82   \n",
       "pre_close        32.95      32.75       34.06       35.82       35.61   \n",
       "change            1.54        0.2       -1.31       -1.76        0.21   \n",
       "pct_chg         4.6737     0.6107     -3.8462     -4.9135      0.5897   \n",
       "vol           132801.0   80978.03     75142.2    75855.89     59515.0   \n",
       "amount      464876.948   269829.7  247467.441  263989.558  211937.608   \n",
       "\n",
       "                  5           6           7           8           9     ...  \\\n",
       "ts_code      002896.SZ   002896.SZ   002896.SZ   002896.SZ   002896.SZ  ...   \n",
       "trade_date    20231018    20231017    20231016    20231013    20231012  ...   \n",
       "open             36.09       37.31       37.42       38.52       39.09  ...   \n",
       "high             36.49        37.8       38.96       39.14        40.3  ...   \n",
       "low               35.5       35.11        37.0        37.4        38.3  ...   \n",
       "close            35.61       36.45       38.06       37.42        38.8  ...   \n",
       "pre_close        36.45       38.06       37.42        38.8       39.45  ...   \n",
       "change           -0.84       -1.61        0.64       -1.38       -0.65  ...   \n",
       "pct_chg        -2.3045     -4.2302      1.7103     -3.5567     -1.6477  ...   \n",
       "vol           55764.69     99718.3   104992.73    111398.0   136569.47  ...   \n",
       "amount      200194.982  361372.791  399899.602  425743.454  532754.049  ...   \n",
       "\n",
       "                 1482       1483       1484       1485       1486       1487  \\\n",
       "ts_code     002896.SZ  002896.SZ  002896.SZ  002896.SZ  002896.SZ  002896.SZ   \n",
       "trade_date   20170913   20170908   20170907   20170906   20170905   20170904   \n",
       "open            39.91      36.28      32.98      29.98      27.25      24.77   \n",
       "high            39.91      36.28      32.98      29.98      27.25      24.77   \n",
       "low             39.91      36.28      32.98      29.98      27.25      24.77   \n",
       "close           39.91      36.28      32.98      29.98      27.25      24.77   \n",
       "pre_close       36.28      32.98      29.98      27.25      24.77      22.52   \n",
       "change           3.63        3.3        3.0       2.73       2.48       2.25   \n",
       "pct_chg         10.01      10.01      10.01      10.02      10.01       9.99   \n",
       "vol           1586.15     924.71      469.7      118.0      114.0       74.0   \n",
       "amount       6330.324   3354.847    1549.07    353.764     310.65    183.298   \n",
       "\n",
       "                 1488       1489       1490       1491  \n",
       "ts_code     002896.SZ  002896.SZ  002896.SZ  002896.SZ  \n",
       "trade_date   20170901   20170831   20170830   20170829  \n",
       "open            22.52      20.47      18.61      15.51  \n",
       "high            22.52      20.47      18.61      16.92  \n",
       "low             22.52      20.47      18.61      15.51  \n",
       "close           22.52      20.47      18.61      16.92  \n",
       "pre_close       20.47      18.61      16.92      11.75  \n",
       "change           2.05       1.86       1.69       5.17  \n",
       "pct_chg         10.01       9.99       9.99       44.0  \n",
       "vol              54.0       36.0       26.0       75.0  \n",
       "amount        121.608     73.692     48.386    126.618  \n",
       "\n",
       "[11 rows x 1492 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-29</th>\n",
       "      <td>15.51</td>\n",
       "      <td>16.92</td>\n",
       "      <td>15.51</td>\n",
       "      <td>16.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-30</th>\n",
       "      <td>18.61</td>\n",
       "      <td>18.61</td>\n",
       "      <td>18.61</td>\n",
       "      <td>18.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31</th>\n",
       "      <td>20.47</td>\n",
       "      <td>20.47</td>\n",
       "      <td>20.47</td>\n",
       "      <td>20.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>22.52</td>\n",
       "      <td>22.52</td>\n",
       "      <td>22.52</td>\n",
       "      <td>22.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-04</th>\n",
       "      <td>24.77</td>\n",
       "      <td>24.77</td>\n",
       "      <td>24.77</td>\n",
       "      <td>24.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19</th>\n",
       "      <td>35.61</td>\n",
       "      <td>36.20</td>\n",
       "      <td>35.10</td>\n",
       "      <td>35.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-20</th>\n",
       "      <td>35.35</td>\n",
       "      <td>36.35</td>\n",
       "      <td>33.80</td>\n",
       "      <td>34.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-23</th>\n",
       "      <td>33.71</td>\n",
       "      <td>34.02</td>\n",
       "      <td>32.25</td>\n",
       "      <td>32.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-24</th>\n",
       "      <td>32.75</td>\n",
       "      <td>34.32</td>\n",
       "      <td>32.58</td>\n",
       "      <td>32.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-25</th>\n",
       "      <td>33.28</td>\n",
       "      <td>36.25</td>\n",
       "      <td>33.22</td>\n",
       "      <td>34.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1492 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             open   high    low  close\n",
       "trade_date                            \n",
       "2017-08-29  15.51  16.92  15.51  16.92\n",
       "2017-08-30  18.61  18.61  18.61  18.61\n",
       "2017-08-31  20.47  20.47  20.47  20.47\n",
       "2017-09-01  22.52  22.52  22.52  22.52\n",
       "2017-09-04  24.77  24.77  24.77  24.77\n",
       "...           ...    ...    ...    ...\n",
       "2023-10-19  35.61  36.20  35.10  35.82\n",
       "2023-10-20  35.35  36.35  33.80  34.06\n",
       "2023-10-23  33.71  34.02  32.25  32.75\n",
       "2023-10-24  32.75  34.32  32.58  32.95\n",
       "2023-10-25  33.28  36.25  33.22  34.49\n",
       "\n",
       "[1492 rows x 4 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据\n",
    "mydata=pd.read_csv(\"./stock002896.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "#pmydata=pd.read_csv(\"./stock300337.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "#plt.plot(mydata[\"close\"])\n",
    "#mydata[\"close\"].plot(figsize=(16,4),legend=True)\n",
    "mydata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1462 entries, 0 to 1461\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  1462 non-null   datetime64[ns]\n",
      " 1   open        1462 non-null   float64       \n",
      " 2   high        1462 non-null   float64       \n",
      " 3   low         1462 non-null   float64       \n",
      " 4   close       1462 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 68.5 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2023-10-19</td>\n",
       "      <td>35.61</td>\n",
       "      <td>36.20</td>\n",
       "      <td>35.10</td>\n",
       "      <td>35.82</td>\n",
       "      <td>36.672</td>\n",
       "      <td>37.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2023-10-20</td>\n",
       "      <td>35.35</td>\n",
       "      <td>36.35</td>\n",
       "      <td>33.80</td>\n",
       "      <td>34.06</td>\n",
       "      <td>36.000</td>\n",
       "      <td>37.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>2023-10-23</td>\n",
       "      <td>33.71</td>\n",
       "      <td>34.02</td>\n",
       "      <td>32.25</td>\n",
       "      <td>32.75</td>\n",
       "      <td>34.938</td>\n",
       "      <td>36.575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>2023-10-24</td>\n",
       "      <td>32.75</td>\n",
       "      <td>34.32</td>\n",
       "      <td>32.58</td>\n",
       "      <td>32.95</td>\n",
       "      <td>34.238</td>\n",
       "      <td>36.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>2023-10-25</td>\n",
       "      <td>33.28</td>\n",
       "      <td>36.25</td>\n",
       "      <td>33.22</td>\n",
       "      <td>34.49</td>\n",
       "      <td>34.014</td>\n",
       "      <td>35.641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10\n",
       "1457 2023-10-19  35.61  36.20  35.10  35.82  36.672  37.611\n",
       "1458 2023-10-20  35.35  36.35  33.80  34.06  36.000  37.103\n",
       "1459 2023-10-23  33.71  34.02  32.25  32.75  34.938  36.575\n",
       "1460 2023-10-24  32.75  34.32  32.58  32.95  34.238  36.137\n",
       "1461 2023-10-25  33.28  36.25  33.22  34.49  34.014  35.641"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# import akshare as ak\n",
    "\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\Anaconda3\\envs\\python310\\lib\\site-packages\\mplfinance\\_arg_validators.py:83: UserWarning: \n",
      "\n",
      " ================================================================= \n",
      "\n",
      "   WARNING: YOU ARE PLOTTING SO MUCH DATA THAT IT MAY NOT BE\n",
      "            POSSIBLE TO SEE DETAILS (Candles, Ohlc-Bars, Etc.)\n",
      "   For more information see:\n",
      "   - https://github.com/matplotlib/mplfinance/wiki/Plotting-Too-Much-Data\n",
      "   \n",
      "   TO SILENCE THIS WARNING, set `type='line'` in `mpf.plot()`\n",
      "   OR set kwarg `warn_too_much_data=N` where N is an integer \n",
      "   LARGER than the number of data points you want to plot.\n",
      "\n",
      " ================================================================ \n",
      "  warnings.warn('\\n\\n ================================================================= '+\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x575 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "import matplotlib.pyplot as plt\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "mpf.plot(mydata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zeng_\\AppData\\Roaming\\Python\\Python310\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 38134 (\\N{CJK UNIFIED IDEOGRAPH-94F6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\zeng_\\AppData\\Roaming\\Python\\Python310\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 37030 (\\N{CJK UNIFIED IDEOGRAPH-90A6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\zeng_\\AppData\\Roaming\\Python\\Python310\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\zeng_\\AppData\\Roaming\\Python\\Python310\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 20221 (\\N{CJK UNIFIED IDEOGRAPH-4EFD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                  opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                  width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(\"银邦股份\")\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=90, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2017-08-29  15.51  16.92  15.51  16.92\n",
      "1    2017-08-30  18.61  18.61  18.61  18.61\n",
      "2    2017-08-31  20.47  20.47  20.47  20.47\n",
      "3    2017-09-01  22.52  22.52  22.52  22.52\n",
      "4    2017-09-04  24.77  24.77  24.77  24.77\n",
      "...         ...    ...    ...    ...    ...\n",
      "1487 2023-10-19  35.61  36.20  35.10  35.82\n",
      "1488 2023-10-20  35.35  36.35  33.80  34.06\n",
      "1489 2023-10-23  33.71  34.02  32.25  32.75\n",
      "1490 2023-10-24  32.75  34.32  32.58  32.95\n",
      "1491 2023-10-25  33.28  36.25  33.22  34.49\n",
      "\n",
      "[1492 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1492 entries, 0 to 1491\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  1492 non-null   datetime64[ns]\n",
      " 1   open        1492 non-null   float64       \n",
      " 2   high        1492 non-null   float64       \n",
      " 3   low         1492 non-null   float64       \n",
      " 4   close       1492 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 69.9 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-29</td>\n",
       "      <td>15.51</td>\n",
       "      <td>16.92</td>\n",
       "      <td>15.51</td>\n",
       "      <td>16.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-08-30</td>\n",
       "      <td>18.61</td>\n",
       "      <td>18.61</td>\n",
       "      <td>18.61</td>\n",
       "      <td>18.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>20.47</td>\n",
       "      <td>20.47</td>\n",
       "      <td>20.47</td>\n",
       "      <td>20.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>22.52</td>\n",
       "      <td>22.52</td>\n",
       "      <td>22.52</td>\n",
       "      <td>22.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>24.77</td>\n",
       "      <td>24.77</td>\n",
       "      <td>24.77</td>\n",
       "      <td>24.77</td>\n",
       "      <td>20.658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>2023-10-19</td>\n",
       "      <td>35.61</td>\n",
       "      <td>36.20</td>\n",
       "      <td>35.10</td>\n",
       "      <td>35.82</td>\n",
       "      <td>36.672</td>\n",
       "      <td>37.611</td>\n",
       "      <td>36.771667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>2023-10-20</td>\n",
       "      <td>35.35</td>\n",
       "      <td>36.35</td>\n",
       "      <td>33.80</td>\n",
       "      <td>34.06</td>\n",
       "      <td>36.000</td>\n",
       "      <td>37.103</td>\n",
       "      <td>36.694000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>2023-10-23</td>\n",
       "      <td>33.71</td>\n",
       "      <td>34.02</td>\n",
       "      <td>32.25</td>\n",
       "      <td>32.75</td>\n",
       "      <td>34.938</td>\n",
       "      <td>36.575</td>\n",
       "      <td>36.555667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>2023-10-24</td>\n",
       "      <td>32.75</td>\n",
       "      <td>34.32</td>\n",
       "      <td>32.58</td>\n",
       "      <td>32.95</td>\n",
       "      <td>34.238</td>\n",
       "      <td>36.137</td>\n",
       "      <td>36.401333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1491</th>\n",
       "      <td>2023-10-25</td>\n",
       "      <td>33.28</td>\n",
       "      <td>36.25</td>\n",
       "      <td>33.22</td>\n",
       "      <td>34.49</td>\n",
       "      <td>34.014</td>\n",
       "      <td>35.641</td>\n",
       "      <td>36.311000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1492 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10         30\n",
       "0    2017-08-29  15.51  16.92  15.51  16.92     NaN     NaN        NaN\n",
       "1    2017-08-30  18.61  18.61  18.61  18.61     NaN     NaN        NaN\n",
       "2    2017-08-31  20.47  20.47  20.47  20.47     NaN     NaN        NaN\n",
       "3    2017-09-01  22.52  22.52  22.52  22.52     NaN     NaN        NaN\n",
       "4    2017-09-04  24.77  24.77  24.77  24.77  20.658     NaN        NaN\n",
       "...         ...    ...    ...    ...    ...     ...     ...        ...\n",
       "1487 2023-10-19  35.61  36.20  35.10  35.82  36.672  37.611  36.771667\n",
       "1488 2023-10-20  35.35  36.35  33.80  34.06  36.000  37.103  36.694000\n",
       "1489 2023-10-23  33.71  34.02  32.25  32.75  34.938  36.575  36.555667\n",
       "1490 2023-10-24  32.75  34.32  32.58  32.95  34.238  36.137  36.401333\n",
       "1491 2023-10-25  33.28  36.25  33.22  34.49  34.014  35.641  36.311000\n",
       "\n",
       "[1492 rows x 8 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zeng_\\AppData\\Roaming\\Python\\Python310\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 38134 (\\N{CJK UNIFIED IDEOGRAPH-94F6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\zeng_\\AppData\\Roaming\\Python\\Python310\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 37030 (\\N{CJK UNIFIED IDEOGRAPH-90A6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\zeng_\\AppData\\Roaming\\Python\\Python310\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\zeng_\\AppData\\Roaming\\Python\\Python310\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 20221 (\\N{CJK UNIFIED IDEOGRAPH-4EFD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "import matplotlib.pyplot as plt\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                  opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                  width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(\"银邦股份\")\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(100)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,1100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "a=np.linspace(0,round(len(df3),0),10,dtype=int).tolist()\n",
    "b=df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "c=data.asfreq(\"2M\").dropna()\n",
    "#c.dropna().index\n",
    "cc=c.index.strftime('%Y-%m-%d').to_numpy()\n",
    "e=df3.trade_date.tolist()\n",
    "#f=[e[i] for i in cc]\n",
    "cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.92],\n",
       "       [18.61],\n",
       "       [20.47],\n",
       "       ...,\n",
       "       [32.75],\n",
       "       [32.95],\n",
       "       [34.49]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,1491)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17407.0</th>\n",
       "      <td>15.51</td>\n",
       "      <td>16.92</td>\n",
       "      <td>15.51</td>\n",
       "      <td>16.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17408.0</th>\n",
       "      <td>18.61</td>\n",
       "      <td>18.61</td>\n",
       "      <td>18.61</td>\n",
       "      <td>18.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17409.0</th>\n",
       "      <td>20.47</td>\n",
       "      <td>20.47</td>\n",
       "      <td>20.47</td>\n",
       "      <td>20.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17410.0</th>\n",
       "      <td>22.52</td>\n",
       "      <td>22.52</td>\n",
       "      <td>22.52</td>\n",
       "      <td>22.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17413.0</th>\n",
       "      <td>24.77</td>\n",
       "      <td>24.77</td>\n",
       "      <td>24.77</td>\n",
       "      <td>24.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19649.0</th>\n",
       "      <td>35.61</td>\n",
       "      <td>36.20</td>\n",
       "      <td>35.10</td>\n",
       "      <td>35.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19650.0</th>\n",
       "      <td>35.35</td>\n",
       "      <td>36.35</td>\n",
       "      <td>33.80</td>\n",
       "      <td>34.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19653.0</th>\n",
       "      <td>33.71</td>\n",
       "      <td>34.02</td>\n",
       "      <td>32.25</td>\n",
       "      <td>32.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19654.0</th>\n",
       "      <td>32.75</td>\n",
       "      <td>34.32</td>\n",
       "      <td>32.58</td>\n",
       "      <td>32.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19655.0</th>\n",
       "      <td>33.28</td>\n",
       "      <td>36.25</td>\n",
       "      <td>33.22</td>\n",
       "      <td>34.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1492 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "17407.0  15.51  16.92  15.51  16.92\n",
       "17408.0  18.61  18.61  18.61  18.61\n",
       "17409.0  20.47  20.47  20.47  20.47\n",
       "17410.0  22.52  22.52  22.52  22.52\n",
       "17413.0  24.77  24.77  24.77  24.77\n",
       "...        ...    ...    ...    ...\n",
       "19649.0  35.61  36.20  35.10  35.82\n",
       "19650.0  35.35  36.35  33.80  34.06\n",
       "19653.0  33.71  34.02  32.25  32.75\n",
       "19654.0  32.75  34.32  32.58  32.95\n",
       "19655.0  33.28  36.25  33.22  34.49\n",
       "\n",
       "[1492 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27caf693100>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:1024, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[1491 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title('银邦股份')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yahoo Finance 数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Here’s how you can do it:\n",
    "\n",
    "+ First, you need to install the yfinance package which is a popular Python library for downloading historical market data from Yahoo Finance. You can install it by running this command in your terminal or command prompt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install yfinance --upgrade --no-cache-dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "msft = yf.Ticker(\"MSFT\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# get all stock info\n",
    "myinfo=msft.info\n",
    "myinfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# get historical market data\n",
    "hist = msft.history(period=\"1mo\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# show meta information about the history (requires history() to be called first)\n",
    "msft.history_metadata\n",
    "\n",
    "# show actions (dividends, splits, capital gains)\n",
    "msft.actions\n",
    "msft.dividends\n",
    "msft.splits\n",
    "msft.capital_gains  # only for mutual funds & etfs\n",
    "\n",
    "# show share count\n",
    "msft.get_shares_full(start=\"2022-01-01\", end=None)\n",
    "\n",
    "# show financials:\n",
    "# - income statement\n",
    "msft.income_stmt\n",
    "msft.quarterly_income_stmt\n",
    "# - balance sheet\n",
    "msft.balance_sheet\n",
    "msft.quarterly_balance_sheet\n",
    "# - cash flow statement\n",
    "msft.cashflow\n",
    "msft.quarterly_cashflow\n",
    "# see `Ticker.get_income_stmt()` for more options\n",
    "\n",
    "# show holders\n",
    "msft.major_holders\n",
    "msft.institutional_holders\n",
    "msft.mutualfund_holders\n",
    "\n",
    "# Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default. \n",
    "# Note: If more are needed use msft.get_earnings_dates(limit=XX) with increased limit argument.\n",
    "msft.earnings_dates\n",
    "\n",
    "# show ISIN code - *experimental*\n",
    "# ISIN = International Securities Identification Number\n",
    "msft.isin\n",
    "\n",
    "# show options expirations\n",
    "msft.options\n",
    "\n",
    "# show news\n",
    "msft.news\n",
    "\n",
    "# get option chain for specific expiration\n",
    "opt = msft.option_chain('YYYY-MM-DD')\n",
    "# data available via: opt.calls, opt.puts"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you can use the yfinance library to download the historical stock data for Apple Inc.  \n",
    " (AAPL) from Yahoo Finance. Here’s an example code snippet that shows how to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "start_date = '2020-01-02'\n",
    "end_date = '2023-01-01'\n",
    "ticker_list = ['AAPL']\n",
    "data = yf.download(ticker_list, start=start_date, end=end_date, ignore_tz=True)[['Close']]\n",
    "# Download historical data for Apple Inc. (AAPL)\n",
    "#data = yf.download(\"AAPL\")\n",
    "for tkr in ticker_list:\n",
    "    dat = yf.Ticker(tkr)\n",
    "    tz = dat._fetch_ticker_tz(debug_mode=True, proxy=None, timeout=30)\n",
    "    valid = yf.utils.is_valid_timezone(tz)\n",
    "    print(f\"{tkr}: tz='{tz}', valid={valid}\")\n",
    "# Print the first 5 rows of the data\n",
    "print(data.head())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code will download the historical stock data for Apple Inc. (AAPL) for the year 2022 and print the first 5 rows of the data.\n",
    "\n",
    "Finally, you can use matplotlib to create a candlestick chart of the stock data. Here’s an example code snippet that shows how to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_finance import candlestick_ohlc\n",
    "import pandas as pd\n",
    "\n",
    "# Convert the data to a candlestick chart format\n",
    "candlestick_data = data.reset_index()\n",
    "candlestick_data['date'] = candlestick_data['Date'].apply(lambda x: pd.Timestamp(x).to_pydatetime())\n",
    "candlestick_data = candlestick_data[['date', 'Open', 'High', 'Low', 'Close']]\n",
    "candlestick_data = [tuple(x) for x in candlestick_data.values]\n",
    "\n",
    "# Create the candlestick chart\n",
    "fig, ax = plt.subplots()\n",
    "candlestick_ohlc(ax, candlestick_data, width=0.6, colorup='green', colordown='red')\n",
    "\n",
    "# Set the x-axis tick labels to be more readable\n",
    "ax.xaxis_date()\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "# Show the chart\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code will convert the downloaded stock data to a candlestick chart format and create a candlestick chart using matplotlib.\n",
    "\n",
    "I hope this helps! Let me know if you have any questions.\n",
    "\n",
    "No problem! Here’s how you can create a deep learning model using PyTorch:\n",
    "\n",
    "First, you need to install PyTorch. You can do this by running the following command in your terminal or command prompt:\n",
    "Next, you need to prepare your data for training the model. You can use the stock data that you downloaded earlier as input to your model. You will also need to split your data into training and testing sets.\n",
    "\n",
    "Once you have your data ready, you can create your deep learning model using PyTorch. Here’s an example code snippet that shows how to create a simple feedforward neural network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "# Define the neural network architecture\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.fc1 = nn.Linear(5, 10)\n",
    "        self.fc2 = nn.Linear(10, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "# Create an instance of the neural network\n",
    "net = Net()\n",
    "\n",
    "# Define the loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code defines a simple feedforward neural network with one hidden layer and an output layer. The input to the network is a tensor of size (batch_size, 5) which corresponds to the five features of your stock data (open, close, high, low, volume). The output of the network is a tensor of size (batch_size, 1) which corresponds to the predicted stock price.\n",
    "\n",
    "Once you have defined your model architecture and loss function, you can train your model using your training data. Here’s an example code snippet that shows how to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the neural network\n",
    "for epoch in range(100):\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        inputs, labels = data\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "\n",
    "    print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code trains the neural network for 100 epochs using stochastic gradient descent (SGD) with a learning rate of 0.001.\n",
    "\n",
    "Finally, you can evaluate your model using your testing data and make predictions on new data. Here’s an example code snippet that shows how to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        inputs, labels = data\n",
    "        outputs = net(inputs)\n",
    "        predicted = torch.round(outputs)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "print('Accuracy: %d %%' % (100 * correct / total))\n",
    "\n",
    "# Make predictions on new data\n",
    "new_data = torch.tensor([[open_price_1, close_price_1, high_price_1, low_price_1, volume_1],\n",
    "                         [open_price_2, close_price_2, high_price_2, low_price_2, volume_2],\n",
    "                         [open_price_3, close_price_3, high_price_3, low_price_3, volume_3]])\n",
    "predictions = net(new_data)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code evaluates the accuracy of your model on your testing data and makes predictions on new data.\n",
    "\n",
    "I hope this helps! Let me know if you have any questions."
   ]
  }
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